

'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     = {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}

class       SCREAMING_SNAKE_CASE       (				lowercase_       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Tuple     =					'''roc_bert'''




      def __init__(       self			:		Any  ,       snake_case			:		Optional[int]=30522  ,       snake_case			:		Tuple=768  ,       snake_case			:		List[str]=12  ,       snake_case			:		Union[str, Any]=12  ,       snake_case			:		List[Any]=3072  ,       snake_case			:		str="gelu"  ,       snake_case			:		str=0.1  ,       snake_case			:		List[str]=0.1  ,       snake_case			:		Union[str, Any]=512  ,       snake_case			:		Union[str, Any]=2  ,       snake_case			:		Union[str, Any]=0.02  ,       snake_case			:		Dict=1e-12  ,       snake_case			:		str=True  ,       snake_case			:		Any=0  ,       snake_case			:		Dict="absolute"  ,       snake_case			:		str=None  ,       snake_case			:		Optional[int]=True  ,       snake_case			:		List[Any]=True  ,       snake_case			:		Optional[int]=768  ,       snake_case			:		Union[str, Any]=910  ,       snake_case			:		Union[str, Any]=512  ,       snake_case			:		List[str]=24858  ,       snake_case			:		str=True  ,       **snake_case			:		Optional[int]  ,       ):



          """simple docstring"""





          _snake_case			:			Dict				       =		vocab_size
          _snake_case			:			Union[str, Any]				       =		max_position_embeddings
          _snake_case			:			Optional[int]				       =		hidden_size
          _snake_case			:			Dict				       =		num_hidden_layers
          _snake_case			:			Optional[Any]				       =		num_attention_heads
          _snake_case			:			Optional[int]				       =		intermediate_size
          _snake_case			:			Optional[int]				       =		hidden_act
          _snake_case			:			int				       =		hidden_dropout_prob
          _snake_case			:			Optional[int]				       =		attention_probs_dropout_prob
          _snake_case			:			Dict				       =		initializer_range
          _snake_case			:			Any				       =		type_vocab_size
          _snake_case			:			Optional[int]				       =		layer_norm_eps
          _snake_case			:			List[str]				       =		use_cache
          _snake_case			:			Any				       =		enable_pronunciation
          _snake_case			:			Any				       =		enable_shape
          _snake_case			:			Optional[int]				       =		pronunciation_embed_dim
          _snake_case			:			Optional[int]				       =		pronunciation_vocab_size
          _snake_case			:			Optional[Any]				       =		shape_embed_dim
          _snake_case			:			Dict				       =		shape_vocab_size
          _snake_case			:			Optional[int]				       =		concat_input
          _snake_case			:			Tuple				       =		position_embedding_type
          _snake_case			:			List[str]				       =		classifier_dropout
          super().__init__(pad_token_id=_A  ,       **_A )











'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								# For consistency across different places the DisjunctiveConstraint is called,
								# dc.token_ids is a list of integers. It is also initialized only by integers.

								UpperCAmelCase_      :       Union[str, Any]          =       [[1, 2, 4], [1, 2, 3, 4]]
								UpperCAmelCase_      :       Dict          =       DisjunctiveConstraint(_A      )
								self.assertTrue(isinstance(dc.token_ids				, _A      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								# We can't have constraints that are complete subsets of another. This leads to a preverse
								# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
								# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
								# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
								# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
								UpperCAmelCase_      :       Optional[Any]          =       [[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(_A      )  # fails here




					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       [[1, 2, 3], [1, 2, 4]]

								UpperCAmelCase_      :       List[Any]          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								UpperCAmelCase_      :       int          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(2      )
								UpperCAmelCase_      :       Optional[int]          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(3      )
								UpperCAmelCase_      :       List[Any]          =       stepped is True and completed is True and reset is False
								self.assertTrue(_A      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]      )





					def 		A      (  self  :		List[Any]      )    ->							Any:
								UpperCAmelCase_      :       int          =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								UpperCAmelCase_      :       str          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(4      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2, 4]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

								dc.reset()

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 3      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 2      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.remaining() == 0      )
								self.assertTrue(dc.current_seq == [1, 2, 5]      )


